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Why Stratification of Networks Emerges in Innovative Society

  • Shingo Takahashi
  • Kyoichi Kijima
  • Ryo Sato
Chapter

Abstract

In this chapter, we model an innovative society like Silicon Valley, California, in terms of a polyagent system, and then apply to it the coordination management framework and the interaction prediction principles introduced in Chap. 1. It is then shown that it is very natural for such a society to produce stratification of networks for intelligent entrepreneurs to cope with the complexity around them. Some of the main theoretical contributions of this research concern how the interaction prediction principle works with subjective game situations described by the polyagent system.

Keywords

Nash Equilibrium Internal Model Decision Situation Noncooperative Game Soft System Methodology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Auger P (1989) Dynamics and thermodynamics in hierarchically organized systems: applications in physics, biology and economics. Pergamon, OxfordGoogle Scholar
  2. 2.
    Bennett PG (1980) Hypergames: developing a model of conflict. Futures 12:489–507CrossRefGoogle Scholar
  3. 3.
    Bennett PG, Dando MR (1979) Complex strategic analysis: a hypergame study of the fall of France. Journal of Operational Research Society 30:23–32MathSciNetMATHGoogle Scholar
  4. 4.
    Bennett PG, Dando MR, Sharp RG (1980) Using hypergames to model difficult social issues: an approach to the case of soccer hooliganism. Journal of Operational Research Society 31:621–635Google Scholar
  5. 5.
    Bennett PG, Cropper S, Huxham C (1989) Modelling interactive decisions: the hypergame focus. In: Rosenhead J (ed) Rational analysis for a problematic world. Wiley, Chichester, pp 283–314Google Scholar
  6. 6.
    Carley KM, Svoboda DM (1996) Modeling organizational adaptation as a simulated annealing process. Sociological Methods and Research 25:138–168CrossRefGoogle Scholar
  7. 7.
    Carver N, Lesser V (1994) The evolution of blackboard control architectures. Expert Systems and Applications 7:1–30CrossRefGoogle Scholar
  8. 8.
    Checkland PB (1981) Systems thinking, systems practice. Wiley, ChichesterGoogle Scholar
  9. 9.
    Checkland PB (1990) Soft systems methodology in action. Wiley, ChichesterGoogle Scholar
  10. 10.
    Fraser NM, Hipel KW (1984) Conflict analysis: models and resolutions. North-Holland, AmsterdamMATHGoogle Scholar
  11. 11.
    Fraser NM, Wang M, Hipel KW (1991) Hypergame theory in 2-person conflicts. Information and Decision Technology 16:301–319MathSciNetGoogle Scholar
  12. 12.
    Galbraith J (1977) Organization design. In: Lockett M, Speur R (eds) Organizations as systems. Addison-Wesley, Reading, pp 105–111Google Scholar
  13. 13.
    Gibbons R (1992) A primer in game theory. Harvester Wheatsheaf, LondonMATHGoogle Scholar
  14. 14.
    Harsanyi J (1967) Games with incomplete information played by Bayesian players, parts I, II, and III. Management Science 14:159–182, 320–334,486–502Google Scholar
  15. 15.
    Howard N (1987) The present and future of metagame analysis. European Journal of Operational Research 32:1–25MathSciNetCrossRefGoogle Scholar
  16. 16.
    Howard N (1989) The manager as politician and general: the metagame approach to analysing cooperation and conflict. In: Rosenhead J (ed) Rational analysis for a problematic world, Wiley, Chichester, pp 239–262Google Scholar
  17. 17.
    Howard N (1990) Soft game theory. Information and Decision Technologies 16:215–227MathSciNetGoogle Scholar
  18. 18.
    Howard N (1994) Drama theory and its relation to game theory: part one. Group Decision and Negotiation 3:187–206CrossRefGoogle Scholar
  19. 19.
    Howard N, Bennett P, Bryant J et al. (1993) Manifesto for a theory of drama and irrational choice. Systems Practice 6:429–434Google Scholar
  20. 20.
    Kijima K (1991) Decision making based on subjective evaluations of problem situation (in Japanese). T. IEE Japan 111-C(3):98–106Google Scholar
  21. 21.
    Kijima K (1996) Intelligent poly-agent learning model and its application. Information and Systems Engineering 2:47–61Google Scholar
  22. 22.
    Kijima K (1999) Poly-agent systems theory: evolution model and its applications. In: Castell AM, Gregory AJ, Hindle GA et al. (eds) Synergy matters: working with systems in the 21st century. Proceedings of UKSS99, Lincoln, UK, Plenum, pp 577–582Google Scholar
  23. 23.
    Levitt RE, Cohen GP, Kunz JC et al. (1994) A theoretical evaluation of measures of organizational design: interrelationship and performance predictability. In: Carley KM, Prietula MJ (eds) Computational organization theory. Lawrence Erlbaum Hillsdale, pp 132–154Google Scholar
  24. 24.
    Mesarovic MD, Takahara Y (1989) Abstract systems theory. Springer, Berlin Heidelberg New YorkMATHCrossRefGoogle Scholar
  25. 25.
    Neches R (1991) Enabling technology for knowledge sharing. Al Magazine 12:36–56Google Scholar
  26. 26.
    Neches R (1993) Knowledge sharing effort. In:http://www-ksl.stanford.edu/knowl-edge-sharing/papers.Cited June, 1998
  27. 27.
    Nishio K (1999) History of Japan (in Japanese). Fusosha, TokyoGoogle Scholar
  28. 28.
    Rosenhead, J (ed) (1989) Rational analysis for a problematic world. Wiley, ChichesterGoogle Scholar
  29. 29.
    Saxenian A (1996) Regional adventure: culture and competition in Silicon Valley and Route 128. Harvard University PressGoogle Scholar
  30. 30.
    Simon HA (1962) The architecture of complexity. Proceedings of the American Philosophical Society 106:467–482Google Scholar
  31. 31.
    Suematsu T (1997) Cultural comparison between organizations in Silicon Valley and Japan: an approach from IT utilization (in Japanese). Journal of Association for Management Informatics 16:23–40Google Scholar
  32. 32.
    Van Gigch JP (1991) System design modeling and metamodeling. Plenum, New YorkGoogle Scholar
  33. 33.
    Wang M, Hipel KW (1992) Misperception and bargaining in the Persian Gulf war. Controland Cybernetics 10:1–26Google Scholar
  34. 34.
    Wang M, Hipel KW, Fraser NM (1988) Modeling misperceptions in games. Journal of Behavior Science 33:207–223MathSciNetCrossRefGoogle Scholar
  35. 35.
    Wonham WN (1976) Towards an abstract internal model principle. IEEE Trans. Systems, Man and Cybernetics IEEE-SMC 6:735–740MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer Japan 2004

Authors and Affiliations

  • Shingo Takahashi
    • 1
  • Kyoichi Kijima
    • 2
  • Ryo Sato
    • 3
  1. 1.School of Science and EngineeringWaseda UniversityShinjuku-ku, TokyoJapan
  2. 2.Faculty of Decision Science and TechnologyTokyo Institute of TechnologyMeguro-ku, TokyoJapan
  3. 3.Institute of Policy and Planning SciencesThe University of TsukubaTsukuba, IbarakiJapan

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